import torch
import torch.nn as nn
import torch.nn.functional as F

mean = {
    'cifar10': (0.4914, 0.4822, 0.4465),
    'cifar100': (0.5071, 0.4867, 0.4408),
    'svhn':(0.438, 0.444, 0.473)
}

std = {
    # cifar10_std = (0.2023, 0.1994, 0.2010) #Some repositories use this value
    'cifar10': (0.2471, 0.2435, 0.2616),
    'cifar100': (0.2675, 0.2565, 0.2761),
    'svhn':(0.198, 0.201, 0.197)
}

class BasicBlock(nn.Module):
    expansion = 1
 
    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
 
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )
 
    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out
 
 
class Bottleneck(nn.Module):
    expansion = 4
 
    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)
 
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )
 
    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out
 
 
class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, normalize_coefs=None, normalize=False):
        super(ResNet, self).__init__()

        self.normalize = normalize

        self.in_planes = 64
 
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)

        dataset = "SVHN"
        self.mu = torch.tensor(mean[dataset.lower()]).view(3,1,1)
        self.std = torch.tensor(std[dataset.lower()]).view(3,1,1)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        
    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)
 
    def forward(self, x, out_feature=False):

        if self.normalize: 
            mu = self.mu.to(x.device)
            std = self.std.to(x.device)
            x = (x-mu)/std

        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        feature = out.view(out.size(0), -1)
        out = self.linear(feature)
        if out_feature == False:
            return out
        else:
            return out,feature
 

def ResNet_8x(n_classes = 10, depth=18, widen_factor=10, normalize = True, dropRate = 0.0):
    if depth == 18:
        strides  = [2,2,2,2]
    else:
        assert (depth == 34)
        strides = [3,4,6,3]
    return ResNet(BasicBlock, strides, n_classes, normalize=normalize)
